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Modelling student retention in tutorial classes with uncertainty – A Bayesian approach to predicting attendance-based retention

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dc.contributor.author Nimy, Eli Bila
dc.date.accessioned 2024-11-06T07:43:13Z
dc.date.available 2024-11-06T07:43:13Z
dc.date.issued 2023
dc.identifier.uri http://hdl.handle.net/20.500.12821/535
dc.description.abstract Bayesian additive regression tree (BART) is a recent statistical method that blends ensemble learning with nonparametric regression. BART is constructed using a Bayesian approach, which provides the benefit of model-based prediction uncertainty, enhancing the reliability of predictions. This study proposes the development of a BART model with a binomial likelihood to predict the percentage of students retained in tutorial classes using attendance data. The proposed model is evaluated and benchmarked against the Random Forest Regressor (RFR). The proposed BART model reported an average of 20% higher predictive performance compared to RFR across five error metrics, achieving an Rsquared score of 0.9414. Furthermore, the study demonstrates the utility of the Highest Density Interval provided by the BART model, which can help in determining the best and worst-case scenarios for student retention rate estimates. The significance of this study extends to multiple stakeholders within the educational sector. Educational institutions, administrators, and policymakers can benefit from this study by gaining insights into how future tutorship programme student retention rates can be predicted using predictive models. Moreover, the foresight provided by the predicted student retention rates can aid in strategic resource allocation, facilitating more informed planning and budgeting for tutorship programmes. en_US
dc.language.iso en en_US
dc.publisher Sol Plaatje University en_US
dc.subject Student retention en_US
dc.subject Tutorship programme en_US
dc.subject Attendance en_US
dc.subject Educational data analytics en_US
dc.subject Bayesian en_US
dc.subject Regression en_US
dc.subject Ensemble en_US
dc.title Modelling student retention in tutorial classes with uncertainty – A Bayesian approach to predicting attendance-based retention en_US
dc.type Thesis en_US


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